Data Architect

Role & responsibilities

Data Modelling & Architecture

Mandatory

Data Governance & Security

Mandatory

Data Warehousing & ETL/ELT Expertise

Mandatory

Cloud Architecture (AWS, Azure)

Mandatory

Snowflake Expertise

Mandatory

Preferred candidate profile

• Lead the design of enterprise data architecture including conceptual, logical, and physical models across analytical and operational domains. 
• Develop, enhance, and govern data models (3NF, Dimensional, Data Vault) ensuring consistency, scalability, and alignment with enterprise standards. 
• Act as the custodian of data models, defining modelling best practices, frameworks, and documentation for delivery teams. 
• Design secure, governed, and compliant data environments including lineage, metadata, RBAC, data masking, and quality frameworks. 
• Architect and optimize data warehousing and ELT pipelines across landing, curated, and semantic layers using modern tools and cloud-native services. 
• Build and maintain cloudbased data platforms using AWS (S3, Glue, Lambda, Athena) and Azure (ADLS, ADF, Synapse) with focus on scalability, performance, and cost optimization. 
• Implement and optimize Snowflake solutions including Streams, Tasks, Snowpipe, multi-cluster warehouses, micro-partitioning, and secure data sharing. 
• Collaborate with engineering, product, and analytics teams to translate business requirements into robust, scalable data solutions. 
• Assess new requirements, perform impact analysis, and provide estimates for model and architecture changes. 
• Promote architectural alignment across teams and contribute to long-term platform and data strategy.

Technical:

• Architect and evolve enterprise data modelsspanning conceptual, logical, and physical layers—to support scalable and futureready data solutions.
• Define, standardize, and govern modelling practices (Dimensional, Data Vault, 3NF), ensuring consistent adoption across delivery and engineering teams.
• Establish secure and governed data environments by implementing robust controls for metadata, lineage, access, privacy, and data quality.
• Lead the architecture of cloudnative data pipelines and warehouse solutions, enabling efficient ELT/ETL processes and reliable endtoend data flows.
• Design and optimize cloud data platforms on AWS and Azure, focusing on performance, scalability, automation, and costefficient operation.
• Develop, enhance, and finetune Snowflake implementations including warehouse strategies, pipeline components (Streams, Tasks, Snowpipe), and workload optimization.

Leadership & Collaboration:

• Provide technical leadership and mentorship to data engineers, guiding them on best practices in data modeling, architecture, cloud engineering, and Snowflake implementation.
• • Collaborate closely with data scientists, product managers, and business stakeholders to operationalize analytical and AI/ML solutions on Snowflake and AWS, ensuring alignment with enterprise architecture and strategic objectives.